{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/4_real_world_data_analysis_and_preparation/5_handling_missing_values.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hHtbJhRcVPBL"
   },
   "source": [
    "# A Comprehensive Guide to Handle Missing Values Effectively"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TaQufesGVPBN"
   },
   "source": [
    "It's not often that we get clean data, real world data is very messy. In fact, missing values are one of the most cases that data scientist and machine learning engineers are likely to deal with day to day, and honestly it takes time to come up with the strategy to remove such missing values effectively. \n",
    "\n",
    "Though we have tools that make the technical side easier, we often end up not checking how the filled values or dropped values fit with the existing data and the target goals of the analysis. Not checking this can leave us communicating wrong information, or spending time doing hyperparameter tuning and model selection thinking that the data is fine, so it must be something else, and end up tuning the parameters weeks and weeks. \n",
    "\n",
    "Having said that, in this tutorial I will walk through the common ways that data scientists and machine learning engineers use to handle missing values and we will see the best practices as we conclude the post. Let's get dive in! I will use california housing, a classic dataset in Machine learning found [here](https://www.kaggle.com/search?q=California+housing+in%3Adatasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "4BpHYmQdVPBO"
   },
   "outputs": [],
   "source": [
    "#from sklearn.datasets import california_housing\n",
    "#This loads the california dataset from sklearn.datasets but since it is preprocessed, there are no missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "JzQO9AwdVPBP"
   },
   "outputs": [],
   "source": [
    "#I will start importing all required libraries. \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "bu5rftw5VPBQ"
   },
   "outputs": [],
   "source": [
    "import urllib.request \n",
    "\n",
    "data_path = 'https://raw.githubusercontent.com/nyandwi/public_datasets/master/housing.csv'\n",
    "\n",
    "def download_read_data(path):\n",
    "    \n",
    "    \"\"\"\n",
    "     Function to retrive data from the data paths\n",
    "     And to read the data as a pandas dataframe\n",
    "  \n",
    "    To return the dataframe\n",
    "    \"\"\" \n",
    "    \n",
    "      ## Only retrieve the directory of the data\n",
    "\n",
    "    data_path =  urllib.request.urlretrieve(path)[0]\n",
    "    data = pd.read_csv(path)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "aW1rcJJfVPBQ",
    "outputId": "506eae06-3388-4a0d-980d-1b8c3fba8cdc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = download_read_data(data_path)\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "qEZeyCehVPBR",
    "outputId": "1b9d1119-a4d0-4b08-b006-f181811f2fdb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W0s_m41JVPBS"
   },
   "source": [
    "It seems that we only have missing values in total_bedrooms. It would be better to have many features missing values fr this experiment, but let's go on. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "91INP6K4VPBS"
   },
   "source": [
    "Now we know the feature which is missing values, let's do some analysis around it and then go through the techniques to handle the feature, but trying to understand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U_TSePIQVPBS"
   },
   "source": [
    "## Eploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "tlVjiznoVPBT",
    "outputId": "41b8ded9-5fea-4956-da26-ee4f374f781c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fe56afe7a10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(housing, vars=['total_bedrooms', 'total_rooms','households','housing_median_age' ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1PGgW9SiVPBT"
   },
   "source": [
    "While our experiment is not about exploratory data analysis at most, you can see that the total bedrooms correlate with total rooms, and that make sense because the bedrooms are counted in total rooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "WlkGLJ3QVPBU",
    "outputId": "6d370427-5f25-4c85-e290-9b79d71e2f5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Total bedrooms')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['total_bedrooms'].plot(kind='hist')\n",
    "plt.xlabel('Total bedrooms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6FodYStJVPBU"
   },
   "source": [
    "You can see that most houses have bedrooms between 0 to 1000. Very few houses have over 1000 bedrooms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "C_SxX6M1VPBV",
    "outputId": "4a1e5f13-fdab-4e6e-eac6-7f0914b1e206"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall that the missing values in our dataset are:\n",
      " \n",
      "longitude               0\n",
      "latitude                0\n",
      "housing_median_age      0\n",
      "total_rooms             0\n",
      "total_bedrooms        207\n",
      "population              0\n",
      "households              0\n",
      "median_income           0\n",
      "median_house_value      0\n",
      "ocean_proximity         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Recall that the missing values in our dataset are:\\n \\n{}'.format(housing.isna().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SJHvwsptVPBV"
   },
   "source": [
    "## Handling Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uHbLwGCeVPBV"
   },
   "source": [
    "There are number of methods to handle missing values, but basically, you can do one of these but keep in mind that each can affect our analysis:\n",
    "    \n",
    "    *Removing the missing values\n",
    "    *Filling the missing values\n",
    "    *Leaving the missing values as it is.\n",
    "    \n",
    "    \n",
    "Starting from the first one, if you were to remove the missing values, fairly simple. You can do the following, and you are done. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "swq_koqiVPBW"
   },
   "source": [
    "### Removing the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Ty1qlC90VPBW"
   },
   "outputs": [],
   "source": [
    "housing_df=housing.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "JWjes6tzVPBW"
   },
   "outputs": [],
   "source": [
    "housin_null_dropped=housing.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "3RUhzJIeVPBW",
    "outputId": "5b122f3e-4d8a-4670-f1fc-50b15f85ed1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housin_null_dropped.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9YnGq1_GVPBX"
   },
   "source": [
    "Now, you're done. However, you can wish to remove the missing values by a condition. In this case, you can do the following. Say you want to remove the columns that contain missing values. Since we don't have more than 1 of such columns, this will not change our dataset, but that's something you can try at your end. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "jxa8DklyVPBX"
   },
   "outputs": [],
   "source": [
    "housin_null_dropped_2=housing.dropna(axis='columns') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ufXQ1gl1VPBX",
    "outputId": "373231c0-71a5-4476-a3f8-fb8853d94b76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housin_null_dropped_2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QHhASJr4VPBY"
   },
   "source": [
    "As you can see, total_bedroom is removed. This is can however led to the loss of data that could be meanigful despite that it contains NaNs. If you wanted more control, then you can use 'threshold' to specify how many non missing values (minimum) to keep for a given column or row. We will not see the effect of this, but you can try that on your end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "H_VjWQIBVPBY"
   },
   "outputs": [],
   "source": [
    "housin_null_dropped_2=housing.dropna(axis='columns', thresh=2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "FKwSeXM1VPBY",
    "outputId": "b03cf6c8-0042-411c-96c9-9c287771320f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housin_null_dropped_2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0l0Zh_e6VPBZ"
   },
   "source": [
    "Another Interesting thing here to try is to remove the rows or columns that are empty. This can save you time if you have hundreds of columns or rows that you want to remove specifically. When how is set to 'all', you are basically saying remove all columns which are all null (contain missing values). **All the thing we saw so far involving the axis='columns', can be applied to rows**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "F0WL66rNVPBZ"
   },
   "outputs": [],
   "source": [
    "housin_null_dropped_3=housing.dropna(axis='columns', how='all') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JpKW_Sv_VPBa"
   },
   "source": [
    "### Filling the missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F9EH3yOIVPBa"
   },
   "source": [
    "With pandas, filling the missing values is quite straight. Here is how you can fill the any missing value with a number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "OlD3YG5AVPBa"
   },
   "outputs": [],
   "source": [
    "housing_filled=housing.fillna(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "EHWhaipWVPBb",
    "outputId": "59735836-dd15-47ed-89e3-3ad5d189330f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_filled.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I_CFNfXKVPBb"
   },
   "source": [
    "You could also use the ffill (forward fill) or backward fill 'bfill', where you fill the values preeceding or following the missing value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "f6IAGmXiVPBb"
   },
   "outputs": [],
   "source": [
    "housing_filled=housing.fillna(method='ffill') #method='bfill' for backward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TFcudNIdVPBb"
   },
   "source": [
    "This is dangerous though. Let's take an example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "k1hjGwdwVPBb",
    "outputId": "51c6ffb7-03a6-4dc5-f85b-3e09bcc0f164"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.80</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5329.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>2323.0</td>\n",
       "      <td>2.0096</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4596.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>2.8345</td>\n",
       "      <td>183800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.81</td>\n",
       "      <td>29.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.0062</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.82</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1.2185</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.82</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3633.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>2.6104</td>\n",
       "      <td>193100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0     -122.23     37.88                41.0        880.0           129.0   \n",
       "1     -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2     -122.24     37.85                52.0       1467.0           190.0   \n",
       "3     -122.25     37.85                52.0       1274.0           235.0   \n",
       "4     -122.25     37.85                52.0       1627.0           280.0   \n",
       "..        ...       ...                 ...          ...             ...   \n",
       "95    -122.26     37.80                36.0       5329.0          2477.0   \n",
       "96    -122.26     37.82                31.0       4596.0          1331.0   \n",
       "97    -122.26     37.81                29.0        335.0           107.0   \n",
       "98    -122.26     37.82                22.0       3682.0          1270.0   \n",
       "99    -122.26     37.82                37.0       3633.0          1085.0   \n",
       "\n",
       "    population  households  median_income  median_house_value ocean_proximity  \n",
       "0        322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1       2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2        496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3        558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4        565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
       "..         ...         ...            ...                 ...             ...  \n",
       "95      3469.0      2323.0         2.0096            130000.0        NEAR BAY  \n",
       "96      2048.0      1180.0         2.8345            183800.0        NEAR BAY  \n",
       "97       202.0        91.0         2.0062            125000.0        NEAR BAY  \n",
       "98      2024.0      1250.0         1.2185            170000.0        NEAR BAY  \n",
       "99      1838.0       980.0         2.6104            193100.0        NEAR BAY  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_filled.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PDZT5RouVPBc"
   },
   "source": [
    "Downside about this is that if the first value would be a missing value, then the rest are same. Also, see what happened at the index 97. The house with total rooms of 335 has a total_bedroom of 1331, which is impossible. All the method introduced above can led to serious bias or wrong results, leading to wrong decisions. Before we wrap how to fill missing values, let's also look at other ways you can use to fill the missing values. Say we want to fill all missing values by mean or median of a given column. \n",
    "\n",
    "In this case, we will use sklearn imputer method to handle this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "xoxIpWKDVPBc",
    "outputId": "d5dc5f67-b34f-40de-e6b3-ff8c904f2ddf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "housing_numeric=housing.drop('ocean_proximity', axis=1) #Simple imputer only work with numeric features, so we drop the OCEAN_PROXIMITY\n",
    "\n",
    "mean_fill=SimpleImputer(missing_values=np.NaN,strategy='mean')\n",
    "\n",
    "mean_fill.fit(housing_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "fAsQclIlVPBc"
   },
   "outputs": [],
   "source": [
    "mean_filled=mean_fill.transform(housing_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "beQ8BhsJVPBc",
    "outputId": "2a6ccc4a-7d53-4490-95cc-9b0f65ec647f"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
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       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>129.0</td>\n",
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       "      <td>452600.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
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       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \n",
       "0           322.0       126.0         8.3252            452600.0  \n",
       "1          2401.0      1138.0         8.3014            358500.0  \n",
       "2           496.0       177.0         7.2574            352100.0  \n",
       "3           558.0       219.0         5.6431            341300.0  \n",
       "4           565.0       259.0         3.8462            342200.0  \n",
       "...           ...         ...            ...                 ...  \n",
       "20635       845.0       330.0         1.5603             78100.0  \n",
       "20636       356.0       114.0         2.5568             77100.0  \n",
       "20637      1007.0       433.0         1.7000             92300.0  \n",
       "20638       741.0       349.0         1.8672             84700.0  \n",
       "20639      1387.0       530.0         2.3886             89400.0  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(mean_filled, columns=housing_numeric.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rAKLfT0NVPBd"
   },
   "source": [
    "As you can see, the total_bedroom is filled with mean. Take for example at index 3 and 4. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9ptd3ZLVPBd"
   },
   "source": [
    "But also this brings an issue. You will also find somewhere that the number of bedrooms are greater than the number of rooms in a house and that's again impossible and can lead to wrong analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cGr5YY3VPBd"
   },
   "source": [
    "## The problem is how do you deal with the missing value effectively? Do you remove them? Or what if there would be other ways to handle it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l__nh_5nVPBd"
   },
   "source": [
    "If you had few missing values, no doubt that it would be good to remove them completely to avoid imputing them with irrelevant values. \"\"Quality over quantity\"\"\n",
    "\n",
    "But also you may wish to keep the data and perhaps find a better way to handle the missing values. One of the best way out there (considering all the flaws of the above methods) is to fill the given missing value considering the values of corresponding features. In this case, we will use sklearn method called Iterative Imputer or Regression Imputer. This works on the principle of Linear regression. I will not explain that here, but here is how it is done. \n",
    "\n",
    "What you can know is this estimates each feature from all the others. You can read more about it [here](https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "sL2TbgqyVPBd",
    "outputId": "e6f6650b-f526-46b8-9c1d-9c52cc31f04e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IterativeImputer()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "iter_imputer = IterativeImputer()\n",
    "iter_imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "UfQdtYOqVPBe"
   },
   "outputs": [],
   "source": [
    "housing_imputed=iter_imputer.fit_transform(housing_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "gjRmzhnUVPBe",
    "outputId": "36cb8b0d-3e92-4ae6-e4fb-bda008e0c41a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
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       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \n",
       "0           322.0       126.0         8.3252            452600.0  \n",
       "1          2401.0      1138.0         8.3014            358500.0  \n",
       "2           496.0       177.0         7.2574            352100.0  \n",
       "3           558.0       219.0         5.6431            341300.0  \n",
       "4           565.0       259.0         3.8462            342200.0  \n",
       "...           ...         ...            ...                 ...  \n",
       "20635       845.0       330.0         1.5603             78100.0  \n",
       "20636       356.0       114.0         2.5568             77100.0  \n",
       "20637      1007.0       433.0         1.7000             92300.0  \n",
       "20638       741.0       349.0         1.8672             84700.0  \n",
       "20639      1387.0       530.0         2.3886             89400.0  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(housing_imputed, columns=housing_numeric.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8J3XBFtEVPBe"
   },
   "source": [
    "As you can see, the missing values are filled in accordance to the other features. For instance, a house with total_room of 1467.0, and bedrooms of 190, and 1274 rooms for 200 for bedrooms. Note that for each values to be missing estimated, every corresponding features is considered. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-T418_QVPBe"
   },
   "source": [
    "## There you have it. You now know how to handle the missing values effectively. \n",
    "\n",
    "Thanks for reading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vN5g8NrLVPBf"
   },
   "outputs": [],
   "source": []
  }
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